Abstract:
In this paper, we propose a novel noise-robust
semi-supervised deep generative model by jointly tackling noisy
labels and outliers simultaneously in a unified robust semi-supervised variational
autoencoder (URSVAE).
Typically, the uncertainty of
of input data is characterized by placing uncertainty prior on the
parameters of the probability density distributions in order to ensure the robustness of the
variational encoder towards outliers. Subsequently, a
noise transition model is integrated naturally into our model to alleviate the detrimental effects of noisy labels. Moreover,
a robust divergence measure is employed to further enhance the robustness, where a novel
variational lower bound is derived and optimized to infer the network parameters. By
proving the influence function on the proposed evidence lower bound is bounded, the enormous potential of the proposed
model in the classification in the presence of the compound noise is demonstrated.
The experimental results highlight the superiority of the proposed
framework by the evaluating on image classification tasks and comparing with the state-of-the-art
approaches.
